However, the spatiotemporal nature of the underlying time series data, with nonlinear behavior causes the prediction task to be nontrivial [1]}. The growing availability of data and advancements in computing power has motivated many researchers to explore a variety of deep learning approaches based on Artificial Neural Networks (ANN) [2]}. Especially, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), such as Long Short Term Memory (LSTM) neural networks, have been successfully used to forecast hourly air temperature with significantly small errors for one time step ahead [3]}, [4]}. In particular, CNNs based models do not require any feature engineering; instead, they rely on feature extraction during the training of the network [5]}. Recently the authors in [6]} proposed a 3d-CNN based model for temperature forecasting.
